###Install packages *** ###Load libraries

library(raster)
Loading required package: sp
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0     ✔ purrr   0.2.5
✔ tibble  2.0.0     ✔ dplyr   0.7.8
✔ tidyr   0.8.2     ✔ stringr 1.3.1
✔ readr   1.3.1     ✔ forcats 0.3.0
package ‘tibble’ was built under R version 3.5.2── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::extract() masks raster::extract()
✖ dplyr::filter()  masks stats::filter()
✖ dplyr::lag()     masks stats::lag()
✖ dplyr::select()  masks raster::select()
library(ggthemes)
library(patchwork)
library(maptools)
Checking rgeos availability: TRUE
library(ggmap)
Google Maps API Terms of Service: http://developers.google.com/maps/terms.
Please cite ggmap if you use it: see citation("ggmap") for details.
library(marmap)

Attaching package: ‘marmap’

The following object is masked from ‘package:raster’:

    as.raster

The following object is masked from ‘package:grDevices’:

    as.raster

###Part 1: Outline maps

usa <- map_data(map="state")

Attaching package: ‘maps’

The following object is masked from ‘package:purrr’:

    map
us_missouri <- map_data(map="state",
                        region="missouri")
us_other <- subset(x=usa,
                   subset=region %in% c("alabama",
                                        "florida",
                                        "mississippi",
                                        "north carolina"))
str(usa)
'data.frame':   15537 obs. of  6 variables:
 $ long     : num  -87.5 -87.5 -87.5 -87.5 -87.6 ...
 $ lat      : num  30.4 30.4 30.4 30.3 30.3 ...
 $ group    : num  1 1 1 1 1 1 1 1 1 1 ...
 $ order    : int  1 2 3 4 5 6 7 8 9 10 ...
 $ region   : chr  "alabama" "alabama" "alabama" "alabama" ...
 $ subregion: chr  NA NA NA NA ...
ggplot()+
  geom_polygon(data=us_other,
               aes(x=long,
                   y=lat,
                   group=group),
               fill = "gray")

ggplot()+
  geom_polygon(data = us_other,
               aes(x=long,
                   y=lat,
                   group=group),
               fill="gray")+
  geom_polygon(data=us_missouri,
               aes(x=long,
                   y=lat,
                   group=group),
               fill="black")

ggplot()+
  geom_polygon(data = us_other,
               aes(x=long,
                   y=lat,
                   group=group),
               fill="gray")+
  geom_polygon(data=us_missouri,
               aes(x=long,
                   y=lat,
                   group=group),
               fill="black")+
  geom_polygon(data = usa,
               aes(x=long,
                   y=lat,
                   group=group),
               fill=NA,
               color="black")

us_map <- ggplot()+
  geom_polygon(data = us_other,
               aes(x=long,
                   y=lat,
                   group=group),
               fill="gray")+
  geom_polygon(data=us_missouri,
               aes(x=long,
                   y=lat,
                   group=group),
               fill="black")+
  geom_polygon(data=usa,
               aes(x=long,
                   y=lat,
                   group=group),
               fill=NA,
               color="black")+
  theme_map()+
  coord_fixed(1.3)
us_map

#Missouri map

missouri <- map_data("county", "missouri")
mo_scott <- subset(missouri, subregion %in% c("scott"))
mo_map <- ggplot()+
  geom_polygon(data=missouri, aes(x=long, y=lat, group=group), fill=NA, color="black")+
  geom_polygon(data=mo_scott, aes(x=long, y=lat, group=group), fill="black")+
  theme_map()+
  coord_fixed(1.3)
mo_map

us_map + mo_map + plot_layout(ncol = 2, widths = c(1.5, 1))

#Outline Maps #This should be interesting… I was born in Hawaii. It isn’t part of the map_data, so I’ll choose Utah, where I lived for two years and learned to ski. I lived in Ogden in Weber county. My initials are AKC. The counties I chose to put in gray are Kane and Carbon.

us_utah <- map_data(map="state",
                      region = "utah")
utah <- map_data("county", "utah")
ut_birth <- subset(utah, subregion %in% c("weber"))
ut_names <- subset(utah, subregion %in% c("carbon", "kane"))

akc_usa <- ggplot()+
  geom_polygon(data = us_utah,
               aes(x = long,
               y = lat,
               group = group),
               fill = "black")+
  geom_polygon(data = usa,
               aes(x = long,
               y = lat,
               group = group),
               fill = NA,
               color = "black")+
  theme_map()+
  coord_fixed(1.3)

akc_counties <- ggplot()+
  geom_polygon(data = ut_birth,
               aes(x = long,
                   y = lat,
                   group = group),
               fill = "black")+
  geom_polygon(data = ut_names,
               aes(x = long,
                   y = lat,
                   group = group),
               fill = "gray")+
  geom_polygon(data = utah,
               aes(x = long,
                   y = lat,
                   group = group),
               fill = NA,
               color = "black")+
  theme_map()+
  coord_fixed(1.3)

akc_usa + akc_counties + plot_layout(ncol = 2, widths = c(1.5,1))

#The Hawaii Gambit… Born on Oahu, and have visited Hawaii (the big island) and Maui. I’ll see if I can get that to work. I love working with maps.

hawaii <- map_data("world", "USA:Hawaii")
hawaii_oahu <- map_data("world", "USA:Hawaii:Oahu")
other_islands <- map_data("world", c("USA:Hawaii:Hawaii", "USA:Hawaii:Maui"))

akc_usa <- ggplot()+
  geom_polygon(data = us_utah,
               aes(x = long,
               y = lat,
               group = group),
               fill = "black")+
  geom_polygon(data = usa,
               aes(x = long,
               y = lat,
               group = group),
               fill = NA,
               color = "black")+
  theme_map()+
  coord_fixed(1.3)

akc_hawaii <- ggplot()+
  geom_polygon(data = hawaii,
               aes(x = long,
                   y = lat,
                   group = group),
               fill = NA,
               color = "black")+
  geom_polygon(data = hawaii_oahu,
               aes(x = long,
                   y = lat,
                   group = group),
               fill = "black")+
  geom_polygon(data = other_islands,
               aes(x = long,
                   y = lat,
                   group = group),
               fill = "gray")+
  theme_map()+
  coord_fixed(1.3)
akc_usa + akc_hawaii + plot_layout(ncol = 2, widths = c(1.5, 1))

#Hmm. The big island of Hawaii didn’t show up as gray. I guess the name in map_data is different. The code is there that would select it. On to Part 2!

###Part 2: Dot distribution maps ###Load the libraries #already loaded ###Define some variables. set.seed value as in assignment so I can see if I’m doing it right!

min_long <- -126
max_long <- -104
min_lat <- 31
max_lat <- 50

set.seed(9618973)
file_path <-"~/Documents/BI685Files/GitHubDocs/chronister_andrew/data"

###Read in the “Gigantopithecus” data

bigfoot <- read.csv(file.path(file_path, "bigfoot.csv"))

bigfoot <- filter(bigfoot,
                  long >= min_long & long <= max_long,
                  lat >= min_lat & lat <= max_lat) %>%
  sample_n(300) %>%
  mutate(name = "Gigantiopithecus") %>%
  select(name, long, lat)

#Read in the bears data

bears <- read_csv(file.path(file_path, "bears.csv"))
Parsed with column specification:
cols(
  name = col_character(),
  long = col_double(),
  lat = col_double()
)
both_species <- bind_rows(bigfoot, bears)

head(both_species)
tail(both_species)

#Get terrain map

base = get_stamenmap(bbox = c(min_long,
                              min_lat,
                              max_long,
                              max_lat),
                     zoom = 4,
                     maptype = "terrain-background")
ggmap(base)

base <- ggmap(base)

base + geom_point(data = bigfoot,
                  aes(x = long,
                      y = lat))

base + geom_point(data = bears,
                  aes(x = long,
                  y = lat))

#Both species

base + geom_point(data = both_species,
                  aes(x = long,
                      y = lat,
                      shape = name,
                      fill = name),
                  size = 2)+
  scale_shape_manual(values = c(22:23))+
  labs(x = "Longitude",
       y = "Latitude",
       fill = "Species",
       shape = "Species")

###Recreate with SO number as seed

---
title: "HW10: Maps"
author: "Andrew Chronister"
date: "`r format(Sys.time(), '%d %B %Y')`"
output: html_notebook
editor_options: 
  chunk_output_type: inline
---

***
###Install packages
***
###Load libraries
```{r}
library(raster)
library(tidyverse)
library(ggthemes)
library(patchwork)
library(maptools)
library(ggmap)
library(marmap)
```
***
###Part 1: Outline maps
```{r}
usa <- map_data(map="state")
us_missouri <- map_data(map="state",
                        region="missouri")
us_other <- subset(x=usa,
                   subset=region %in% c("alabama",
                                        "florida",
                                        "mississippi",
                                        "north carolina"))
```

```{r}
str(usa)
```
```{r}
ggplot()+
  geom_polygon(data=us_other,
               aes(x=long,
                   y=lat,
                   group=group),
               fill = "gray")
```

```{r}
ggplot()+
  geom_polygon(data = us_other,
               aes(x=long,
                   y=lat,
                   group=group),
               fill="gray")+
  geom_polygon(data=us_missouri,
               aes(x=long,
                   y=lat,
                   group=group),
               fill="black")
```

```{r}
ggplot()+
  geom_polygon(data = us_other,
               aes(x=long,
                   y=lat,
                   group=group),
               fill="gray")+
  geom_polygon(data=us_missouri,
               aes(x=long,
                   y=lat,
                   group=group),
               fill="black")+
  geom_polygon(data = usa,
               aes(x=long,
                   y=lat,
                   group=group),
               fill=NA,
               color="black")
```

```{r}
us_map <- ggplot()+
  geom_polygon(data = us_other,
               aes(x=long,
                   y=lat,
                   group=group),
               fill="gray")+
  geom_polygon(data=us_missouri,
               aes(x=long,
                   y=lat,
                   group=group),
               fill="black")+
  geom_polygon(data=usa,
               aes(x=long,
                   y=lat,
                   group=group),
               fill=NA,
               color="black")+
  theme_map()+
  coord_fixed(1.3)
us_map
```
#Missouri map 
```{r}
missouri <- map_data("county", "missouri")
mo_scott <- subset(missouri, subregion %in% c("scott"))
mo_map <- ggplot()+
  geom_polygon(data=missouri, aes(x=long, y=lat, group=group), fill=NA, color="black")+
  geom_polygon(data=mo_scott, aes(x=long, y=lat, group=group), fill="black")+
  theme_map()+
  coord_fixed(1.3)
mo_map
```

```{r}
us_map + mo_map + plot_layout(ncol = 2, widths = c(1.5, 1))
```
#Outline Maps
#This should be interesting... I was born in Hawaii. It isn't part of the map_data, so I'll choose Utah, where I lived for two years and learned to ski.  I lived in Ogden in Weber county.  My initials are AKC. The counties I chose to put in gray are Kane and Carbon.   
```{r}
us_utah <- map_data(map="state",
                      region = "utah")
utah <- map_data("county", "utah")
ut_birth <- subset(utah, subregion %in% c("weber"))
ut_names <- subset(utah, subregion %in% c("carbon", "kane"))

akc_usa <- ggplot()+
  geom_polygon(data = us_utah,
               aes(x = long,
               y = lat,
               group = group),
               fill = "black")+
  geom_polygon(data = usa,
               aes(x = long,
               y = lat,
               group = group),
               fill = NA,
               color = "black")+
  theme_map()+
  coord_fixed(1.3)

akc_counties <- ggplot()+
  geom_polygon(data = ut_birth,
               aes(x = long,
                   y = lat,
                   group = group),
               fill = "black")+
  geom_polygon(data = ut_names,
               aes(x = long,
                   y = lat,
                   group = group),
               fill = "gray")+
  geom_polygon(data = utah,
               aes(x = long,
                   y = lat,
                   group = group),
               fill = NA,
               color = "black")+
  theme_map()+
  coord_fixed(1.3)

akc_usa + akc_counties + plot_layout(ncol = 2, widths = c(1.5,1))
```
#The Hawaii Gambit... Born on Oahu, and have visited Hawaii (the big island) and Maui. I'll see if I can get that to work.  I love working with maps.    
```{r}
hawaii <- map_data("world", "USA:Hawaii")
hawaii_oahu <- map_data("world", "USA:Hawaii:Oahu")
other_islands <- map_data("world", c("USA:Hawaii:Hawaii", "USA:Hawaii:Maui"))

akc_usa <- ggplot()+
  geom_polygon(data = us_utah,
               aes(x = long,
               y = lat,
               group = group),
               fill = "black")+
  geom_polygon(data = usa,
               aes(x = long,
               y = lat,
               group = group),
               fill = NA,
               color = "black")+
  theme_map()+
  coord_fixed(1.3)

akc_hawaii <- ggplot()+
  geom_polygon(data = hawaii,
               aes(x = long,
                   y = lat,
                   group = group),
               fill = NA,
               color = "black")+
  geom_polygon(data = hawaii_oahu,
               aes(x = long,
                   y = lat,
                   group = group),
               fill = "black")+
  geom_polygon(data = other_islands,
               aes(x = long,
                   y = lat,
                   group = group),
               fill = "gray")+
  theme_map()+
  coord_fixed(1.3)
akc_usa + akc_hawaii + plot_layout(ncol = 2, widths = c(1.5, 1))
```
#Hmm.  The big island of Hawaii didn't show up as gray.  I guess the name in map_data is different.  The code is there that would select it.  On to Part 2!

***
###Part 2: Dot distribution maps
***
###Load the libraries 
#already loaded
***
###Define some variables. set.seed value as in assignment so I can see if I'm doing it right! 
```{r}
min_long <- -126
max_long <- -104
min_lat <- 31
max_lat <- 50

set.seed(9618973)
file_path <-"~/Documents/BI685Files/GitHubDocs/chronister_andrew/data"
```

***
###Read in the "Gigantopithecus" data
```{r}
bigfoot <- read.csv(file.path(file_path, "bigfoot.csv"))

bigfoot <- filter(bigfoot,
                  long >= min_long & long <= max_long,
                  lat >= min_lat & lat <= max_lat) %>%
  sample_n(300) %>%
  mutate(name = "Gigantiopithecus") %>%
  select(name, long, lat)
```
#Read in the bears data
```{r}
bears <- read_csv(file.path(file_path, "bears.csv"))

both_species <- bind_rows(bigfoot, bears)

head(both_species)
```
```{r}
tail(both_species)
```
#Get terrain map
```{r}
base = get_stamenmap(bbox = c(min_long,
                              min_lat,
                              max_long,
                              max_lat),
                     zoom = 4,
                     maptype = "terrain-background")
ggmap(base)
```
```{r}
base <- ggmap(base)

base + geom_point(data = bigfoot,
                  aes(x = long,
                      y = lat))
```
```{r}
base + geom_point(data = bears,
                  aes(x = long,
                  y = lat))
```
#Both species
```{r}
base + geom_point(data = both_species,
                  aes(x = long,
                      y = lat,
                      shape = name,
                      fill = name),
                  size = 2)+
  scale_shape_manual(values = c(22:23))+
  labs(x = "Longitude",
       y = "Latitude",
       fill = "Species",
       shape = "Species")
```
***
###Recreate with SO number as seed



